/**
* This file is part of ORB-SLAM.
* It is based on the file orb.cpp from the OpenCV library (see BSD license below).
*
* Copyright (C) 2014 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <http://webdiis.unizar.es/~raulmur/orbslam/>
*
* ORB-SLAM is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM. If not, see <http://www.gnu.org/licenses/>.
*/

/*********************************************************************
* Software License Agreement (BSD License)
*
*  Copyright (c) 2009, Willow Garage, Inc.
*  All rights reserved.
*
*  Redistribution and use in source and binary forms, with or without
*  modification, are permitted provided that the following conditions
*  are met:
*
*   * Redistributions of source code must retain the above copyright
*     notice, this list of conditions and the following disclaimer.
*   * Redistributions in binary form must reproduce the above
*     copyright notice, this list of conditions and the following
*     disclaimer in the documentation and/or other materials provided
*     with the distribution.
*   * Neither the name of the Willow Garage nor the names of its
*     contributors may be used to endorse or promote products derived
*     from this software without specific prior written permission.
*
*  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
*  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
*  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
*  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
*  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
*  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
*  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
*  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
*  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
*  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
*  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
*  POSSIBILITY OF SUCH DAMAGE.
*
*********************************************************************/


#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <vector>

#include "ORBextractor.h"

#include <vikit/vision.h> //for shitomasiscore
//#include <ros/ros.h>


using namespace cv;
using namespace std;

namespace ORB_SLAM
{

const float HARRIS_K = 0.04f;

const int PATCH_SIZE = 31;
const int HALF_PATCH_SIZE = 15;
const int EDGE_THRESHOLD = 16;

static void
HarrisResponses(const Mat& img, vector<KeyPoint>& pts, int blockSize, float harris_k)
{
    CV_Assert( img.type() == CV_8UC1 && blockSize*blockSize <= 2048 );

    size_t ptidx, ptsize = pts.size();

    const uchar* ptr00 = img.ptr<uchar>();
    int step = (int)(img.step/img.elemSize1());
    int r = blockSize/2;

    float scale = (1 << 2) * blockSize * 255.0f;
    scale = 1.0f / scale;
    float scale_sq_sq = scale * scale * scale * scale;

    AutoBuffer<int> ofsbuf(blockSize*blockSize);
    int* ofs = ofsbuf;
    for( int i = 0; i < blockSize; i++ )
        for( int j = 0; j < blockSize; j++ )
            ofs[i*blockSize + j] = (int)(i*step + j);

    for( ptidx = 0; ptidx < ptsize; ptidx++ )
    {
        int x0 = cvRound(pts[ptidx].pt.x - r);
        int y0 = cvRound(pts[ptidx].pt.y - r);

        const uchar* ptr0 = ptr00 + y0*step + x0;
        int a = 0, b = 0, c = 0;

        for( int k = 0; k < blockSize*blockSize; k++ )
        {
            const uchar* ptr = ptr0 + ofs[k];
            int Ix = (ptr[1] - ptr[-1])*2 + (ptr[-step+1] - ptr[-step-1]) + (ptr[step+1] - ptr[step-1]);
            int Iy = (ptr[step] - ptr[-step])*2 + (ptr[step-1] - ptr[-step-1]) + (ptr[step+1] - ptr[-step+1]);
            a += Ix*Ix;
            b += Iy*Iy;
            c += Ix*Iy;
        }
        pts[ptidx].response = ((float)a * b - (float)c * c -
                               harris_k * ((float)a + b) * ((float)a + b))*scale_sq_sq;
    }
}



static float IC_Angle(const Mat& image, Point2f pt,  const vector<int> & u_max)
{
    int m_01 = 0, m_10 = 0;

    const uchar* center = &image.at<uchar> (cvRound(pt.y), cvRound(pt.x));

    // Treat the center line differently, v=0
    for (int u = -HALF_PATCH_SIZE; u <= HALF_PATCH_SIZE; ++u)
        m_10 += u * center[u];

    // Go line by line in the circuI853lar patch
    int step = (int)image.step1();
    for (int v = 1; v <= HALF_PATCH_SIZE; ++v)
    {
        // Proceed over the two lines
        int v_sum = 0;
        int d = u_max[v];
        for (int u = -d; u <= d; ++u)
        {
            int val_plus = center[u + v*step], val_minus = center[u - v*step];
            v_sum += (val_plus - val_minus);
            m_10 += u * (val_plus + val_minus);
        }
        m_01 += v * v_sum;
    }

    return fastAtan2((float)m_01, (float)m_10);
}


const float factorPI = (float)(CV_PI/180.f);
void computeOrbDescriptor(const KeyPoint& kpt,
                                 const Mat& img, const Point* pattern,
                                 uchar* desc)
{
    float angle = (float)kpt.angle*factorPI;
    float a = (float)cos(angle), b = (float)sin(angle);

    const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
    const int step = (int)img.step;

    #define GET_VALUE(idx) \
        center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \
               cvRound(pattern[idx].x*a - pattern[idx].y*b)]


    for (int i = 0; i < 32; ++i, pattern += 16)
    {
        int t0, t1, val;
        t0 = GET_VALUE(0); t1 = GET_VALUE(1);
        val = t0 < t1;
        t0 = GET_VALUE(2); t1 = GET_VALUE(3);
        val |= (t0 < t1) << 1;
        t0 = GET_VALUE(4); t1 = GET_VALUE(5);
        val |= (t0 < t1) << 2;
        t0 = GET_VALUE(6); t1 = GET_VALUE(7);
        val |= (t0 < t1) << 3;
        t0 = GET_VALUE(8); t1 = GET_VALUE(9);
        val |= (t0 < t1) << 4;
        t0 = GET_VALUE(10); t1 = GET_VALUE(11);
        val |= (t0 < t1) << 5;
        t0 = GET_VALUE(12); t1 = GET_VALUE(13);
        val |= (t0 < t1) << 6;
        t0 = GET_VALUE(14); t1 = GET_VALUE(15);
        val |= (t0 < t1) << 7;

        desc[i] = (uchar)val;
    }

    #undef GET_VALUE
}


static int bit_pattern_31_[256*4] =
{
    8,-3, 9,5/*mean (0), correlation (0)*/,
    4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/,
    -11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/,
    7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/,
    2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/,
    1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/,
    -2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/,
    -13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/,
    -13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/,
    10,4, 11,9/*mean (0.122065), correlation (0.093285)*/,
    -13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/,
    -11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/,
    7,7, 12,6/*mean (0.160583), correlation (0.130064)*/,
    -4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/,
    -13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/,
    -9,0, -7,5/*mean (0.198234), correlation (0.143636)*/,
    12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/,
    -3,6, -2,12/*mean (0.166847), correlation (0.171682)*/,
    -6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/,
    11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/,
    4,7, 5,1/*mean (0.205106), correlation (0.186848)*/,
    5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/,
    3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/,
    -8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/,
    -2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/,
    -13,12, -8,10/*mean (0.14783), correlation (0.206356)*/,
    -7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/,
    -4,2, -3,7/*mean (0.188237), correlation (0.21384)*/,
    -10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/,
    5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/,
    5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/,
    1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/,
    9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/,
    4,7, 4,12/*mean (0.131005), correlation (0.257622)*/,
    2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/,
    -4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/,
    -8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/,
    4,11, 9,12/*mean (0.226226), correlation (0.258255)*/,
    0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/,
    -13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/,
    -3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/,
    -6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/,
    8,12, 10,7/*mean (0.225337), correlation (0.282851)*/,
    0,9, 1,3/*mean (0.226687), correlation (0.278734)*/,
    7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/,
    -13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/,
    10,7, 12,1/*mean (0.125517), correlation (0.31089)*/,
    -6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/,
    10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/,
    -13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/,
    -13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/,
    3,3, 7,8/*mean (0.177755), correlation (0.309394)*/,
    5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/,
    -1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/,
    3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/,
    2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/,
    -13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/,
    -13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/,
    -13,3, -11,8/*mean (0.134222), correlation (0.322922)*/,
    -7,12, -4,7/*mean (0.153284), correlation (0.337061)*/,
    6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/,
    -9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/,
    -2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/,
    -12,5, -7,5/*mean (0.207805), correlation (0.335631)*/,
    3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/,
    -7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/,
    -3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/,
    2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/,
    -11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/,
    -1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/,
    5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/,
    -4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/,
    -9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/,
    -12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/,
    10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/,
    7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/,
    -7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/,
    -4,9, -3,4/*mean (0.099865), correlation (0.372276)*/,
    7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/,
    -7,6, -5,1/*mean (0.126125), correlation (0.369606)*/,
    -13,11, -12,5/*mean (0.130364), correlation (0.358502)*/,
    -3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/,
    7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/,
    -13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/,
    1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/,
    2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/,
    -4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/,
    -1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/,
    7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/,
    1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/,
    9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/,
    -1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/,
    -13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/,
    7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/,
    12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/,
    6,3, 7,11/*mean (0.1074), correlation (0.413224)*/,
    5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/,
    2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/,
    3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/,
    2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/,
    9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/,
    -8,4, -7,9/*mean (0.183682), correlation (0.402956)*/,
    -11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/,
    1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/,
    6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/,
    2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/,
    6,3, 11,0/*mean (0.204588), correlation (0.411762)*/,
    3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/,
    7,8, 9,3/*mean (0.213237), correlation (0.409306)*/,
    -11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/,
    -10,11, -5,10/*mean (0.247672), correlation (0.413392)*/,
    -5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/,
    -10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/,
    8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/,
    4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/,
    -10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/,
    4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/,
    -2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/,
    -5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/,
    7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/,
    -9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/,
    -5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/,
    8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/,
    -9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/,
    1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/,
    7,-4, 9,1/*mean (0.132692), correlation (0.454)*/,
    -2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/,
    11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/,
    -12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/,
    3,7, 7,12/*mean (0.147627), correlation (0.456643)*/,
    5,5, 10,8/*mean (0.152901), correlation (0.455036)*/,
    0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/,
    -9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/,
    0,7, 2,12/*mean (0.18312), correlation (0.433855)*/,
    -1,2, 1,7/*mean (0.185504), correlation (0.443838)*/,
    5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/,
    3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/,
    -13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/,
    -5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/,
    -4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/,
    6,5, 8,0/*mean (0.1972), correlation (0.450481)*/,
    -7,6, -6,12/*mean (0.199438), correlation (0.458156)*/,
    -13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/,
    1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/,
    4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/,
    -2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/,
    2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/,
    -2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/,
    4,1, 9,3/*mean (0.23962), correlation (0.444824)*/,
    -6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/,
    -3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/,
    7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/,
    4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/,
    -13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/,
    7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/,
    7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/,
    -7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/,
    -8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/,
    -13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/,
    2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/,
    10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/,
    -6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/,
    8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/,
    2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/,
    -11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/,
    -12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/,
    -11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/,
    5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/,
    -2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/,
    -1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/,
    -13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/,
    -10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/,
    -3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/,
    2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/,
    -9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/,
    -4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/,
    -4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/,
    -6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/,
    6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/,
    -13,11, -5,5/*mean (0.162427), correlation (0.501907)*/,
    11,11, 12,6/*mean (0.16652), correlation (0.497632)*/,
    7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/,
    -1,12, 0,7/*mean (0.169456), correlation (0.495339)*/,
    -4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/,
    -7,1, -6,7/*mean (0.175), correlation (0.500024)*/,
    -13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/,
    -7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/,
    -8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/,
    -5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/,
    -13,7, -8,10/*mean (0.196739), correlation (0.496503)*/,
    1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/,
    1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/,
    9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/,
    5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/,
    -1,11, 1,-13/*mean (0.212), correlation (0.499414)*/,
    -9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/,
    -1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/,
    -13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/,
    8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/,
    2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/,
    7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/,
    -10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/,
    -10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/,
    4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/,
    3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/,
    -4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/,
    5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/,
    4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/,
    -9,9, -4,3/*mean (0.236977), correlation (0.497739)*/,
    0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/,
    -12,1, -6,1/*mean (0.243297), correlation (0.489447)*/,
    3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/,
    -10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/,
    8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/,
    -8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/,
    2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/,
    10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/,
    6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/,
    -7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/,
    -3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/,
    -1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/,
    -3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/,
    -8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/,
    4,2, 12,12/*mean (0.01778), correlation (0.546921)*/,
    2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/,
    6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/,
    3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/,
    11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/,
    -3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/,
    4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/,
    2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/,
    -10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/,
    -13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/,
    -13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/,
    6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/,
    0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/,
    -13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/,
    -9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/,
    -13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/,
    5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/,
    2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/,
    -1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/,
    9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/,
    11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/,
    3,0, 3,5/*mean (0.101147), correlation (0.525576)*/,
    -1,4, 0,10/*mean (0.105263), correlation (0.531498)*/,
    3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/,
    -13,0, -10,5/*mean (0.112798), correlation (0.536582)*/,
    5,8, 12,11/*mean (0.114181), correlation (0.555793)*/,
    8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/,
    7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/,
    -10,4, -10,9/*mean (0.12094), correlation (0.554785)*/,
    7,3, 12,4/*mean (0.122582), correlation (0.555825)*/,
    9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/,
    7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/,
    -1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/
};

ORBextractor::ORBextractor(int _nfeatures, float _scaleFactor, int _nlevels, int _scoreType,
         int _fastTh, const float sigmaLevel0):
    nlevels(_nlevels), nfeatures(_nfeatures), scaleFactor(_scaleFactor),
    scoreType(_scoreType), fastTh(_fastTh)
{
    //Scale Levels Info
    mvScaleFactor.resize(nlevels);
    mvScaleFactor[0]=1;
    mvLevelSigma2.resize(nlevels);
    mvLevelSigma2[0]=sigmaLevel0*sigmaLevel0;
    for(int i=1; i<nlevels; i++){
        mvScaleFactor[i]=mvScaleFactor[i-1]*scaleFactor;
        mvLevelSigma2[i]=sigmaLevel0*sigmaLevel0*mvScaleFactor[i]*mvScaleFactor[i];
    }

    float invScaleFactor = 1.0f/scaleFactor;
    mvInvScaleFactor.resize(nlevels);
    mvInvScaleFactor[0]=1;
    for(int i=1; i<nlevels; ++i)
        mvInvScaleFactor[i]=mvInvScaleFactor[i-1]*invScaleFactor;

    mvInvLevelSigma2.resize(mvLevelSigma2.size());
    for(int i=0; i<nlevels; ++i)
        mvInvLevelSigma2[i]=1/mvLevelSigma2[i];

    mnFeaturesPerLevel.resize(nlevels);
    float factor = (float)(1.0 / scaleFactor);
    float nDesiredFeaturesPerScale = nfeatures*(1 - factor)/(1 - (float)pow((double)factor, (double)nlevels));

    int sumFeatures = 0;
    for( int level = 0; level < nlevels-1; level++ )
    {
        mnFeaturesPerLevel[level] = cvRound(nDesiredFeaturesPerScale);
        sumFeatures += mnFeaturesPerLevel[level];
        nDesiredFeaturesPerScale *= factor;
    }
    mnFeaturesPerLevel[nlevels-1] = std::max(nfeatures - sumFeatures, 0);

    const int npoints = 512;
    const Point* pattern0 = (const Point*)bit_pattern_31_;
    std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));

    //This is for orientation
    // pre-compute the end of a row in a circular patch
    umax.resize(HALF_PATCH_SIZE + 1);

    int v, v0, vmax = cvFloor(HALF_PATCH_SIZE * sqrt(2.f) / 2 + 1);
    int vmin = cvCeil(HALF_PATCH_SIZE * sqrt(2.f) / 2);
    const double hp2 = HALF_PATCH_SIZE*HALF_PATCH_SIZE;
    for (v = 0; v <= vmax; ++v)
        umax[v] = cvRound(sqrt(hp2 - v * v));

    // Make sure we are symmetric
    for (v = HALF_PATCH_SIZE, v0 = 0; v >= vmin; --v)
    {
        while (umax[v0] == umax[v0 + 1])
            ++v0;
        umax[v] = v0;
        ++v0;
    }
}

void computeOrientation(const Mat& image, vector<KeyPoint>& keypoints, const vector<int>& umax)
{
    for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
         keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
    {
        keypoint->angle = IC_Angle(image, keypoint->pt, umax);
    }
}

// used for initializing evenly distributed binned seed keypoints
// however we choose the scale factor, this function does not totally prevent spawning very close keypoints accross levels
/*void ORBextractor::operator()( std::vector<cv::KeyPoint>& _keypoints,
                               const std::vector<cv::Mat> & vImagePyramid,
                               const std::vector<cv::Mat> & vBlurredImagePyramid,
OutputArray _descriptors,  const float detection_threshold)
{
    std::vector<cv::KeyPoint> corners(grid_n_cols_*grid_n_rows_, cv::KeyPoint());
    vector<vector<KeyPoint> > allKeypoints;
#if 1
    ComputeKeyPointsBF(allKeypoints, vImagePyramid);// this generates more points than ComputeKeyPoints
#else
    ComputeKeyPoints(allKeypoints);
#endif
    for(int L=0; L<nlevels; ++L)
    {
        const float scale = mvScaleFactor[L];
        vector<KeyPoint> &keypoints=allKeypoints[L];
        for(vector<KeyPoint>::const_iterator it=keypoints.begin(), ite=keypoints.end(); it!=ite; ++it)
        {
            const cv::Point2f & xy = it->pt;
            const int k = static_cast<int>((xy.y*scale)/cell_size_)*grid_n_cols_
                    + static_cast<int>((xy.x*scale)/cell_size_);
            if(grid_occupancy_[k])
                continue;
            const float score = vk::shiTomasiScore(vImagePyramid[L], xy.x, xy.y);
            if(score > corners.at(k).response)
                corners.at(k) = *it;
        }
        keypoints.clear();
        keypoints.reserve(grid_n_cols_*grid_n_rows_/2);
    }

    // Create feature for every corner that has high enough corner score, refill allKeyPoints
    std::for_each(corners.begin(), corners.end(), [&](cv::KeyPoint& c) {
        if(c.response > detection_threshold)
            allKeypoints[c.octave].push_back(c);
    });
    resetGrid();
    //compute ORB descriptors
    Mat descriptors;
    int nkeypoints = 0;
    for (int level = 0; level < nlevels; ++level)
        nkeypoints += (int)allKeypoints[level].size();
    if( nkeypoints == 0 )
        _descriptors.release();
    else
    {
        _descriptors.create(nkeypoints, 32, CV_8U);
        descriptors = _descriptors.getMat();
    }
    _keypoints.clear();
    _keypoints.reserve(nkeypoints);

    int offset = 0;
    for (int level = 0; level < nlevels; ++level)
    {
        vector<KeyPoint>& keypoints = allKeypoints[level];
        int nkeypointsLevel = (int)keypoints.size();

        if(nkeypointsLevel==0)
            continue;
        const Mat& workingMat = vBlurredImagePyramid[level];

        // Compute the descriptors
        Mat desc = descriptors.rowRange(offset, offset + nkeypointsLevel);
        computeDescriptors(workingMat, keypoints, desc, pattern);

        offset += nkeypointsLevel;

        // Scale keypoint coordinates
        if (level != 0)
        {
            float scale = mvScaleFactor[level]; //getScale(level, firstLevel, scaleFactor);
            for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
                 keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
                keypoint->pt *= scale;
        }
        // And add the keypoints to the output
        _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
    }
}*/

void ORBextractor::ComputeKeyPointsBF(vector<vector<KeyPoint> >& allKeypoints,
                                      const std::vector<cv::Mat>& vImagePyramid)
{
    allKeypoints.resize(nlevels);
    size_t total(0);
    for(int L=0; L<nlevels; ++L)
    {
        std::vector<cv::KeyPoint> &keysCell=allKeypoints[L];
#if 0
        const float scale = mvScaleFactor[L];

        vector<fast::fast_xy> fast_corners;
#if __SSE2__
        fast::fast_corner_detect_10_sse2(
                    (fast::fast_byte*) img_pyr[L].data, img_pyr[L].cols,
                    img_pyr[L].rows, img_pyr[L].cols, 20, fast_corners);
#elif HAVE_FAST_NEON
        fast::fast_corner_detect_9_neon(
                    (fast::fast_byte*) img_pyr[L].data, img_pyr[L].cols,
                    img_pyr[L].rows, img_pyr[L].cols, 20, fast_corners);
#else
        fast::fast_corner_detect_10(
                    (fast::fast_byte*) img_pyr[L].data, img_pyr[L].cols,
                    img_pyr[L].rows, img_pyr[L].cols, 20, fast_corners);
#endif
        vector<int> scores, nm_corners;
        fast::fast_corner_score_10((fast::fast_byte*) img_pyr[L].data, img_pyr[L].cols, fast_corners, 20, scores);
        fast::fast_nonmax_3x3(fast_corners, scores, nm_corners);

        for(auto it=nm_corners.begin(), ite=nm_corners.end(); it!=ite; ++it)
        {
            fast::fast_xy& xy = fast_corners.at(*it);
            const int k = static_cast<int>((xy.y*scale)/cell_size_)*grid_n_cols_
                    + static_cast<int>((xy.x*scale)/cell_size_);
            if(grid_occupancy_[k])
                continue;
            const float score = vk::shiTomasiScore(img_pyr[L], xy.x, xy.y);
            if(score > corners.at(k).score)
                corners.at(k) = Corner(xy.x*scale, xy.y*scale, score, L, 0.0f);
        }
#else
        FAST(vImagePyramid[L], keysCell,fastTh,true);
        for(size_t k=0, kend=keysCell.size(); k<kend; ++k)
        {
            keysCell[k].octave=L;
            keysCell[k].size*=mvScaleFactor[L];
        }
        total+= keysCell.size();
#endif
        computeOrientation(vImagePyramid[L], keysCell, umax);
    }
#if 1
    //apply non max sup accross levels    
    cv::Mat responseMap(vImagePyramid[0].rows,vImagePyramid[0].cols, CV_16S, cv::Scalar(-1));
    cv::Mat indexMap(vImagePyramid[0].rows,vImagePyramid[0].cols, CV_16S, cv::Scalar(-1));
    cv::Mat nmsMap(vImagePyramid[0].rows,vImagePyramid[0].cols, CV_8UC1, cv::Scalar(0));
    vector<int> nm_corners;
    nm_corners.reserve(total);
    size_t index=0;
    for(int L=0; L<nlevels; ++L)
    {
        const float scale = mvScaleFactor[L];
        for(std::vector<cv::KeyPoint>::const_iterator it=allKeypoints[L].begin(),
            ite=allKeypoints[L].end(); it!=ite; ++it, ++index)
        {
            const cv::KeyPoint &c= *it;
            int xx=(int)(c.pt.x*scale), yy=(int)(c.pt.y*scale);
            if(responseMap.at<short>(yy,xx) < (short)c.response){
            responseMap.at<short>(yy,xx) = (short)c.response;
            indexMap.at<short>(yy,xx) =index;
            }           
        }
    }
    assert(index==total);
    nonMaximaSuppression(responseMap, 3, nmsMap);

    for(int ry=0; ry< vImagePyramid[0].rows; ++ ry )
    {
        for(int cx=0; cx< vImagePyramid[0].cols; ++ cx )
        {
            if(nmsMap.at<uchar>(ry, cx)>0){
                nm_corners.push_back(indexMap.at<short>(ry,cx));
            }
        }
    }
    std::sort(nm_corners.begin(), nm_corners.end());
    int level(0), start_pos(0), finish_pos(allKeypoints[level].size());
    vector<vector<KeyPoint> > allKeypointsSub;
    allKeypointsSub.resize(nlevels);
    allKeypointsSub[level].reserve(allKeypoints[level].size());

    for(auto it=nm_corners.begin(), ite=nm_corners.end(); it!=ite; ++it)
    {
        if(*it>= finish_pos)
        {
            start_pos=finish_pos;
            ++level;
            finish_pos += allKeypoints[level].size();

            allKeypointsSub[level].reserve(allKeypoints[level].size());
        }
        allKeypointsSub[level].push_back(allKeypoints[level][*it-start_pos]);
    }
    assert(finish_pos== (int)total);
    allKeypoints=allKeypointsSub;
#if 0
    cv::Mat outimg;
    std::vector<cv::KeyPoint> vKeys;
    for(int L=0; L<nlevels; ++L)
    {
        vKeys.insert(vKeys.end(), allKeypoints[L].begin(), allKeypoints[L].end());
    }
    int jim=0;
    for (auto it=vKeys.begin(), ite= vKeys.end(); it!=ite; ++it, ++jim)
    {
        cout<< jim<<" ";
        cout<< it->pt.x<<" "<< it->pt.y<<endl;
        it->pt.x*=mvScaleFactor[it->octave];
        it->pt.y*=mvScaleFactor[it->octave];
    }
    cv::drawKeypoints(vImagePyramid[0], vKeys,outimg);
    cv::imshow("raw keypoints", outimg);

    cv::waitKey();
#endif
#endif
}
// must know each key's level
// given keypoints position and level, compute their orientation and descriptors
void ORBextractor::MakeKeyPoints_Rest(std::vector<cv::KeyPoint> & vKeys,
                                      const std::vector<cv::Mat>& vImagePyramid,
                                      const std::vector<cv::Mat>& vBlurredImagePyramid,
                                      cv::OutputArray _descriptors)
{
    Mat descriptors;
    int nkeypoints = vKeys.size();
    if( nkeypoints == 0 )
        _descriptors.release();
    else
    {
        _descriptors.create(nkeypoints, 32, CV_8U);
        descriptors = _descriptors.getMat();
    }
    int offset = 0;
    for(auto it= vKeys.begin(), ite=vKeys.end(); it!=ite; ++it, ++offset){
        size_t L= it->octave;
        const float inv_scale = mvInvScaleFactor[L];
        const float scale= mvScaleFactor[L];
        it->size =(int)(PATCH_SIZE*scale);

        it->pt =it->pt*inv_scale;
        it->angle = IC_Angle(vImagePyramid[L], it->pt, umax);
        // Compute the descriptors
        Mat desc = descriptors.row(offset);
        computeOrbDescriptor(*it, vBlurredImagePyramid[L], &pattern[0], desc.ptr(0));
        it->pt =it->pt*scale;
    }
}
void ORBextractor::ComputeKeyPoints(vector<vector<KeyPoint> >& allKeypoints, bool bGAFD)
{
    allKeypoints.resize(nlevels);

    float imageRatio = (float)mvImagePyramid[0].cols/mvImagePyramid[0].rows;
    //HUAI: I suspect it should be rows/cols, but tows/cols gives worse result in a few tests

    for (int level = 0; level < nlevels; ++level)
    {
        const int nDesiredFeatures = mnFeaturesPerLevel[level];

        const int levelCols = sqrt((float)nDesiredFeatures/(5*imageRatio));
        const int levelRows = imageRatio*levelCols;

        const int minBorderX = EDGE_THRESHOLD;
        const int minBorderY = minBorderX;
        const int maxBorderX = mvImagePyramid[level].cols-EDGE_THRESHOLD;
        const int maxBorderY = mvImagePyramid[level].rows-EDGE_THRESHOLD;

        const int W = maxBorderX - minBorderX;
        const int H = maxBorderY - minBorderY;
        const int cellW = ceil((float)W/levelCols);
        const int cellH = ceil((float)H/levelRows);

        const int nCells = levelRows*levelCols;
        const int nfeaturesCell = ceil((float)nDesiredFeatures/nCells);

        vector<vector<vector<KeyPoint> > > cellKeyPoints(levelRows, vector<vector<KeyPoint> >(levelCols));

        vector<vector<int> > nToRetain(levelRows,vector<int>(levelCols));
        vector<vector<int> > nTotal(levelRows,vector<int>(levelCols));
        vector<vector<bool> > bNoMore(levelRows,vector<bool>(levelCols,false));
        vector<int> iniXCol(levelCols);
        vector<int> iniYRow(levelRows);
        int nNoMore = 0;
        int nToDistribute = 0;


        float hY = cellH + 6;
        float hX = cellW + 6;

        for(int i=0; i<levelRows; i++)
        {
            const float iniY = minBorderY + i*cellH - 3;
            iniYRow[i] = iniY;

            if(i == levelRows-1)
            {
                hY = maxBorderY+3-iniY;
                if(hY<=0)
                    continue;
            }

            for(int j=0; j<levelCols; j++)
            {
                float iniX;

                if(i==0)
                {
                    iniX = minBorderX + j*cellW - 3;
                    iniXCol[j] = iniX;
                }
                else
                {
                    iniX = iniXCol[j];
                }


                if(j == levelCols-1)
                {
                    hX = maxBorderX+3-iniX;
                    if(hX<=0)
                        continue;
                }


                Mat cellImage = mvImagePyramid[level].rowRange(iniY,iniY+hY).colRange(iniX,iniX+hX);

//                Mat cellMask;
//                if(!mvMaskPyramid[level].empty())
//                    cellMask = cv::Mat(mvMaskPyramid[level],Rect(iniX,iniY,hX,hY));

                cellKeyPoints[i][j].reserve(nfeaturesCell*5);

                FAST(cellImage,cellKeyPoints[i][j],fastTh,true);

                if(cellKeyPoints[i][j].size()<=3)
                {
                    cellKeyPoints[i][j].clear();

                    FAST(cellImage,cellKeyPoints[i][j],7,true);
                }

                if( scoreType == ORB::HARRIS_SCORE )
                {
                    // Compute the Harris cornerness
                    HarrisResponses(cellImage,cellKeyPoints[i][j], 7, HARRIS_K);
                }

                const int nKeys = cellKeyPoints[i][j].size();
                nTotal[i][j] = nKeys;

                if(nKeys>nfeaturesCell)
                {
                    nToRetain[i][j] = nfeaturesCell;
                    bNoMore[i][j] = false;
                }
                else
                {
                    nToRetain[i][j] = nKeys;
                    nToDistribute += nfeaturesCell-nKeys;
                    bNoMore[i][j] = true;
                    nNoMore++;
                }

            }
        }


        // Retain by score

        while(nToDistribute>0 && nNoMore<nCells)
        {
            int nNewFeaturesCell = nfeaturesCell + ceil((float)nToDistribute/(nCells-nNoMore));
            nToDistribute = 0;

            for(int i=0; i<levelRows; i++)
            {
                for(int j=0; j<levelCols; j++)
                {
                    if(!bNoMore[i][j])
                    {
                        if(nTotal[i][j]>nNewFeaturesCell)
                        {
                            nToRetain[i][j] = nNewFeaturesCell;
                            bNoMore[i][j] = false;
                        }
                        else
                        {
                            nToRetain[i][j] = nTotal[i][j];
                            nToDistribute += nNewFeaturesCell-nTotal[i][j];
                            bNoMore[i][j] = true;
                            nNoMore++;
                        }
                    }
                }
            }
        }

        vector<KeyPoint> & keypoints = allKeypoints[level];
        keypoints.reserve(nDesiredFeatures*2);

        const int scaledPatchSize = PATCH_SIZE*mvScaleFactor[level];

        // Retain by score and transform coordinates
        for(int i=0; i<levelRows; i++)
        {
            for(int j=0; j<levelCols; j++)
            {
                vector<KeyPoint> &keysCell = cellKeyPoints[i][j];
                KeyPointsFilter::retainBest(keysCell,nToRetain[i][j]);
                if((int)keysCell.size()>nToRetain[i][j])
                    keysCell.resize(nToRetain[i][j]);

                for(size_t k=0, kend=keysCell.size(); k<kend; k++)
                {
                    keysCell[k].pt.x+=iniXCol[j];
                    keysCell[k].pt.y+=iniYRow[i];
                    keysCell[k].octave=level;
                    keysCell[k].size = scaledPatchSize;
                    keypoints.push_back(keysCell[k]);
                }
            }
        }
        if((int)keypoints.size()>nDesiredFeatures)
        {
            KeyPointsFilter::retainBest(keypoints,nDesiredFeatures);
            keypoints.resize(nDesiredFeatures);
        }
    }

    // and compute orientations
    if(!bGAFD){
        for (int level = 0; level < nlevels; ++level)
            computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);
    }
}

void computeDescriptors(const Mat& image, const vector<KeyPoint>& keypoints, Mat& descriptors,
                               const vector<Point>& pattern)
{
    descriptors = Mat::zeros((int)keypoints.size(), 32, CV_8UC1);

    for (size_t i = 0; i < keypoints.size(); i++)
        computeOrbDescriptor(keypoints[i], image, &pattern[0], descriptors.ptr((int)i));
}

void ORBextractor::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _keypoints,
                      OutputArray _descriptors)
{ 
    if(_image.empty())
        return;

    Mat image = _image.getMat();
    assert(image.type() == CV_8UC1 );

    // Pre-compute the scale pyramids    
    ComputePyramid(image);

    vector < vector<KeyPoint> > allKeypoints;
    ComputeKeyPoints(allKeypoints);

    Mat descriptors;

    int nkeypoints = 0;
    for (int level = 0; level < nlevels; ++level)
        nkeypoints += (int)allKeypoints[level].size();
    if( nkeypoints == 0 )
        _descriptors.release();
    else
    {
        _descriptors.create(nkeypoints, 32, CV_8U);
        descriptors = _descriptors.getMat();
    }

    _keypoints.clear();
    _keypoints.reserve(nkeypoints);

    int offset = 0;
    for (int level = 0; level < nlevels; ++level)
    {
        vector<KeyPoint>& keypoints = allKeypoints[level];
        int nkeypointsLevel = (int)keypoints.size();

        if(nkeypointsLevel==0)
            continue;

        // preprocess the resized image
        Mat& workingMat = mvBlurredImagePyramid[level];

        // Compute the descriptors
        Mat desc = descriptors.rowRange(offset, offset + nkeypointsLevel);
        computeDescriptors(workingMat, keypoints, desc, pattern);

        offset += nkeypointsLevel;

        // Scale keypoint coordinates
        if (level != 0)
        {
            float scale = mvScaleFactor[level]; //getScale(level, firstLevel, scaleFactor);
            for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
                 keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
                keypoint->pt *= scale;
        }
        // And add the keypoints to the output
        _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
    }
}
// input original keypoints in image vKeys, output undistorted keypoints vKeysUn
void UndistortKeyPoints(const std::vector<cv::KeyPoint>& vKeys, std::vector<cv::KeyPoint>& vKeysUn,
                               const cv::Mat & K, const cv::Mat & distCoef)
{
    if(distCoef.at<float>(0)==0.0)
    {
        vKeysUn=vKeys;
        return;
    }

    // Fill matrix with points
    cv::Mat mat(vKeys.size(),2,CV_32F);
    for(unsigned int i=0; i<vKeys.size(); i++)
    {
        mat.at<float>(i,0)=vKeys[i].pt.x;
        mat.at<float>(i,1)=vKeys[i].pt.y;
    }

    // Undistort points
    mat=mat.reshape(2);

    cv::undistortPoints(mat,mat,K,distCoef,cv::Mat(),K);
    mat=mat.reshape(1);
    // Fill undistorted keypoint vector
    vKeysUn.resize(vKeys.size());
    for(unsigned int i=0; i<vKeys.size(); i++)
    {
        cv::KeyPoint kp = vKeys[i];
        kp.pt.x=mat.at<float>(i,0);
        kp.pt.y=mat.at<float>(i,1);
        vKeysUn[i]=kp;
    }
}

//assign gravity aligned orientation (GAO) angle to both keys and undistorted keys
// This function works for points close to image border
//input: vKeysUn stores already undistorted keypoints
// output: vKeys and vKeyUn stores GAO
void computeKeyPointGAO(std::vector< cv::KeyPoint>& vKeys, std::vector< cv::KeyPoint>& vKeysUn,
                        const cv::Mat& K, const cv::Mat& distCoef, Eigen::Vector3d ginc){

    ginc.normalize();
    cv::Mat norm_point(1,3,CV_32F), distort_point(1,2, CV_32F);
    float fx=K.at<float>(0,0), cx=K.at<float>(0,2),
            fy=K.at<float>(1,1), cy=K.at<float>(1,2);
    float factor=max(fx*ginc(0)+ cx*ginc(2), fy*ginc(1)+ cy*ginc(2));
    cv::Mat rvec=cv::Mat::zeros(3,1, CV_32F);//dummy
    cv::Mat tvec=cv::Mat::zeros(3,1, CV_32F);
    int jade=0;
    for (vector<cv::KeyPoint>::iterator keypoint = vKeys.begin(),
         keypointEnd = vKeys.end(); keypoint != keypointEnd; ++keypoint, ++jade)
    {
        norm_point.at<float>(0)=( vKeysUn[jade].pt.x- cx)/ fx;
        norm_point.at<float>(1)=( vKeysUn[jade].pt.y- cy)/ fy;
        norm_point.at<float>(2)=1;
        Eigen::Vector3d temp=ginc*2/factor; // how many pixel from p we expect p' to move
        norm_point.at<float>(0)+=temp[0];
        norm_point.at<float>(1)+=temp[1];
        norm_point.at<float>(2)+=temp[2];
        cv::projectPoints(norm_point, rvec, tvec, K, distCoef, distort_point);
        //cout<<"Distorted :"<< distort_point<<endl;
        keypoint->angle = atan2(distort_point.at<float>(1)-keypoint->pt.y,
                                distort_point.at<float>(0)-keypoint->pt.x);
        vKeysUn[jade].angle = keypoint->angle;
//        cout<<"GAO angle:"<< keypoint->angle<<endl;
    }
}
// extract gravity aligned feature descriptors for keypoints in an image
void ORBextractor::operator()(InputArray _image,cv::InputArray mask,
  std::vector<cv::KeyPoint>& _keypoints,  cv::OutputArray _descriptors,
  std::vector<cv::KeyPoint>& _keypointsUn,
  const cv::Mat & K, const cv::Mat & distCoef, const Eigen::Vector3d& ginc)
{
    if(_image.empty())
        return;

    Mat image = _image.getMat();
    assert(image.type() == CV_8UC1 );

    // Pre-compute the scale pyramids
    ComputePyramid(image);

    vector < vector<KeyPoint> > allKeypoints;
    ComputeKeyPoints(allKeypoints, true);
    // copy all keypoints into output array
    int nkeypoints = 0;
    for (int level = 0; level < nlevels; ++level)
        nkeypoints += (int)allKeypoints[level].size();
    _keypoints.clear();
    _keypoints.reserve(nkeypoints);
    int offset = 0;
    for (int level = 0; level < nlevels; ++level)
    {
        vector<KeyPoint>& keypoints = allKeypoints[level];
        int nkeypointsLevel = (int)keypoints.size();

        if(nkeypointsLevel==0)
            continue;
        // add the keypoints to the output
        _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
        // Scale keypoint coordinates
        if (level != 0)
        {
            float scale = mvScaleFactor[level]; //getScale(level, firstLevel, scaleFactor);
            for (vector<KeyPoint>::iterator keypoint = _keypoints.begin()+offset,
                 keypointEnd = _keypoints.end(); keypoint != keypointEnd; ++keypoint)
                keypoint->pt *= scale;
        }
        offset += nkeypointsLevel;
    }

    //for all keypoints, undistort and compute orientation

    UndistortKeyPoints( _keypoints, _keypointsUn, K, distCoef);
    computeKeyPointGAO(_keypoints, _keypointsUn, K, distCoef, ginc);
    offset = 0;
    for (int level = 0; level < nlevels; ++level)
    {
        vector<KeyPoint>& keypoints = allKeypoints[level];
        int nkeypointsLevel = (int)keypoints.size();

        if(nkeypointsLevel==0)
            continue;
        // assign keypoint angles
        for (vector<KeyPoint>::iterator keypoint1 = _keypoints.begin()+offset,
             keypoint2 = keypoints.begin(), key2end= keypoints.end();
             keypoint2!=key2end; ++keypoint1, ++keypoint2)
            keypoint2->angle = keypoint1->angle;
        offset += nkeypointsLevel;
    }

    Mat descriptors;
    if( nkeypoints == 0 )
        _descriptors.release();
    else
    {
        _descriptors.create(nkeypoints, 32, CV_8U);
        descriptors = _descriptors.getMat();
    }

    offset = 0;
    for (int level = 0; level < nlevels; ++level)
    {
        vector<KeyPoint>& keypoints = allKeypoints[level];
        int nkeypointsLevel = (int)keypoints.size();

        if(nkeypointsLevel==0)
            continue;

        // preprocess the resized image
        Mat& workingMat = mvImagePyramid[level];   
        // Compute the descriptors
        Mat desc = descriptors.rowRange(offset, offset + nkeypointsLevel);
        computeDescriptors(workingMat, keypoints, desc, pattern);

        offset += nkeypointsLevel;   
    }
}
// bGAFD if true gravity aligned feature direction provided, this function only works for one pyramid level
void ORBextractor::operator()( InputArray _image, vector<KeyPoint>& _keypoints,
                      OutputArray _descriptors, bool bGAFD)
{
    if(_image.empty())
        return;
    if(!bGAFD){// compute orientation
        for (vector<KeyPoint>::iterator keypoint = _keypoints.begin(),
             keypointEnd = _keypoints.end(); keypoint != keypointEnd; ++keypoint)
        {
            Mat image = _image.getMat();
            keypoint->angle = IC_Angle(image, keypoint->pt, umax);
        }
    }
    Mat image = _image.getMat();
    assert(image.type() == CV_8UC1 );
    // Pre-compute the scale pyramids
    ComputePyramid(image);
    Mat descriptors;
    int nkeypoints = _keypoints.size();
    if( nkeypoints == 0 )
        _descriptors.release();
    else
    {
        _descriptors.create(nkeypoints, 32, CV_8U);
        descriptors = _descriptors.getMat();
    }
    // preprocess the resized image
    Mat& workingMat = mvBlurredImagePyramid[0];

    // Compute the descriptors
    descriptors = Mat::zeros((int)_keypoints.size(), 32, CV_8UC1);
    for (size_t i = 0; i < _keypoints.size(); i++){
        if(_keypoints[i].size==0){ //FAQ: is it possible some keypoints in libviso2 do not have ORB descriptor?
            cerr<<"Some keypoint has zero size in ORB extractor!"<<endl;
            continue;
        }
        computeOrbDescriptor(_keypoints[i], workingMat, &pattern[0], descriptors.ptr((int)i));
    }
}
void ORBextractor::ClonePyramid(std::vector<cv::Mat> & vImagePyramid)
{
    vImagePyramid.resize(nlevels);
    for (int level = 0; level < nlevels; ++level)
    {
        vImagePyramid[level]= mvImagePyramid[level].clone();
    }
}

void ORBextractor::ComputePyramid(cv::Mat image)
{
    mvImagePyramid.resize(nlevels);
    mvBlurredImagePyramid.resize(nlevels);
    for (int level = 0; level < nlevels; ++level)
    {
        float scale = mvInvScaleFactor[level];
        Size sz(cvRound((float)image.cols*scale), cvRound((float)image.rows*scale));
        Size wholeSize(sz.width + EDGE_THRESHOLD*2, sz.height + EDGE_THRESHOLD*2);
        Mat temp(wholeSize, image.type());
        mvImagePyramid[level] = temp(Rect(EDGE_THRESHOLD, EDGE_THRESHOLD, sz.width, sz.height));

//        Mat masktemp;
//        if( !Mask.empty() )
//        {
//            masktemp = Mat(wholeSize, Mask.type());
//            mvMaskPyramid[level] = masktemp(Rect(EDGE_THRESHOLD, EDGE_THRESHOLD, sz.width, sz.height));
//        }

        // Compute the resized image
        if( level != 0 )
        {
            resize(mvImagePyramid[level-1], mvImagePyramid[level], sz, 0, 0, INTER_LINEAR);
//            if (!Mask.empty())
//            {
//                resize(mvMaskPyramid[level-1], mvMaskPyramid[level], sz, 0, 0, INTER_NEAREST);
//            }

            copyMakeBorder(mvImagePyramid[level], temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
                           BORDER_REFLECT_101+BORDER_ISOLATED);
//            if (!Mask.empty())
//                copyMakeBorder(mvMaskPyramid[level], masktemp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
//                               BORDER_CONSTANT+BORDER_ISOLATED);
        }
        else
        {
            copyMakeBorder(image, temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
                           BORDER_REFLECT_101);
//            if( !Mask.empty() )
//                copyMakeBorder(Mask, masktemp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
//                               BORDER_CONSTANT+BORDER_ISOLATED);
        }
        // preprocess the resized image
        mvBlurredImagePyramid[level] = mvImagePyramid[level].clone();
        GaussianBlur(mvBlurredImagePyramid[level], mvBlurredImagePyramid[level],
                     Size(7, 7), 2, 2, BORDER_REFLECT_101);

    }  
}
void ORBextractor::ComputePyramid(const cv::Mat & image,   std::vector<cv::Mat>& vImagePyramid )
{
    vImagePyramid.resize(nlevels);
    for (int level = 0; level < nlevels; ++level)
    {
        float scale = mvInvScaleFactor[level];
        Size sz(cvRound((float)image.cols*scale), cvRound((float)image.rows*scale));
        Size wholeSize(sz.width + EDGE_THRESHOLD*2, sz.height + EDGE_THRESHOLD*2);
        Mat temp(wholeSize, image.type());
        vImagePyramid[level] = temp(Rect(EDGE_THRESHOLD, EDGE_THRESHOLD, sz.width, sz.height));

        // Compute the resized image
        if( level != 0 )
        {
            resize(vImagePyramid[level-1], vImagePyramid[level], sz, 0, 0, INTER_LINEAR);
            copyMakeBorder(vImagePyramid[level], temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
                           BORDER_REFLECT_101+BORDER_ISOLATED);
        }
        else
        {
            copyMakeBorder(image, temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD,
                           BORDER_REFLECT_101);
        }
    }
}
void ORBextractor::ComputeBlurredPyramid(const std::vector<cv::Mat> & vImagePyramid,
                                         std::vector<cv::Mat> & vBlurredImagePyramid)
{
    //compute blurred image pyramid
    assert((int)vImagePyramid.size()==nlevels);

    vBlurredImagePyramid.resize(nlevels);
    for (int level = 0; level < nlevels; ++level)
    {
        // preprocess the resized image
        vBlurredImagePyramid[level] = vImagePyramid[level].clone();
        GaussianBlur(vBlurredImagePyramid[level], vBlurredImagePyramid[level],
                     Size(7, 7), 2, 2, BORDER_REFLECT_101);
    }
}

/*! @brief suppress non-maximal values
 *
 * nonMaximaSuppression produces a mask (dst) such that every non-zero
 * value of the mask corresponds to a local maxima of src. The criteria
 * for local maxima is as follows:
 *
 * 	For every possible (sz x sz) region within src, an element is a
 * 	local maxima of src iff it is strictly greater than all other elements
 * 	of windows which intersect the given element
 *
 * Intuitively, this means that all maxima must be at least sz+1 pixels
 * apart, though the spacing may be greater
 *
 * A gradient image or a constant image has no local maxima by the definition
 * given above
 *
 * The method is derived from the following paper:
 * A. Neubeck and L. Van Gool. "Efficient Non-Maximum Suppression," ICPR 2006
 *
 * Example:
 * \code
 * 	// create a random test image
 * 	Mat random(Size(2000,2000), DataType<float>::type);
 * 	randn(random, 1, 1);
 *
 * 	// only look for local maxima above the value of 1
 * 	Mat mask = (random > 1);
 *
 * 	// find the local maxima with a window of 50
 * 	Mat maxima;
 * 	nonMaximaSuppression(random, 50, maxima, mask);
 *
 * 	// optionally set all non-maxima to zero
 * 	random.setTo(0, maxima == 0);
 * \endcode
 *
 * @param src the input image/matrix, of any valid cv type
 * @param sz the size of the window
 * @param dst the mask of type CV_8U, where non-zero elements correspond to
 * local maxima of the src
 * @param mask an input mask to skip particular elements
 */
void nonMaximaSuppression(const Mat& src, const int sz, Mat& dst, const Mat mask) {

    // initialise the block mask and destination
    const int M = src.rows;
    const int N = src.cols;
    const bool masked = !mask.empty();
    Mat block = 255*Mat_<uint8_t>::ones(Size(2*sz+1,2*sz+1));
    dst = Mat_<uint8_t>::zeros(src.size());

    // iterate over image blocks
    for (int m = 0; m < M; m+=sz+1) {
        for (int n = 0; n < N; n+=sz+1) {
            Point  ijmax;
            double vcmax, vnmax;

            // get the maximal candidate within the block
            Range ic(m, min(m+sz+1,M));
            Range jc(n, min(n+sz+1,N));
            minMaxLoc(src(ic,jc), NULL, &vcmax, NULL, &ijmax, masked ? mask(ic,jc) : noArray());
            Point cc = ijmax + Point(jc.start,ic.start);

            // search the neighbours centered around the candidate for the true maxima
            Range in(max(cc.y-sz,0), min(cc.y+sz+1,M));
            Range jn(max(cc.x-sz,0), min(cc.x+sz+1,N));

            // mask out the block whose maxima we already know
            Mat_<uint8_t> blockmask;
            block(Range(0,in.size()), Range(0,jn.size())).copyTo(blockmask);
            Range iis(ic.start-in.start, min(ic.start-in.start+sz+1, in.size()));
            Range jis(jc.start-jn.start, min(jc.start-jn.start+sz+1, jn.size()));
            blockmask(iis, jis) = Mat_<uint8_t>::zeros(Size(jis.size(),iis.size()));

            minMaxLoc(src(in,jn), NULL, &vnmax, NULL, &ijmax, masked ? mask(in,jn).mul(blockmask) : blockmask);
//            Point cn = ijmax + Point(jn.start, in.start);

            // if the block centre is also the neighbour centre, then it's a local maxima
            if (vcmax > vnmax) {
                dst.at<uint8_t>(cc.y, cc.x) = 255;
            }
        }
    }
}
} //namespace ORB_SLAM
